from typing import Dict, List, Optional, Union

import numpy as np

from ...image_processing_utils import BaseImageProcessor, BatchFeature, get_size_dict
from ...image_transforms import (
    center_crop,
    convert_to_rgb,
    get_resize_output_image_size,
    normalize,
    rescale,
    resize,
    to_channel_dimension_format,
)
from ...image_utils import (
    OPENAI_CLIP_MEAN,
    OPENAI_CLIP_STD,
    ChannelDimension,
    ImageInput,
    PILImageResampling,
    make_list_of_images,
    to_numpy_array,
    valid_images,
)
from ...utils import TensorType, is_vision_available, logging


lowerCamelCase__  =					logging.get_logger(__name__)


if is_vision_available():
   import PIL




class 					_UpperCAmelCase			(     lowerCAmelCase  ):



 '''simple docstring'''




 __A			       =					['''pixel_values''']







 def __init__(		self				:   Union[str, Any]							, lowercase_				:   bool = True							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   PILImageResampling = PILImageResampling.BICUBIC							, lowercase_				:   bool = True							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   bool = True							, lowercase_				:   Union[int, float] = 1 / 255							, lowercase_				:   bool = True							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   bool = True							, **lowercase_				:   Any							, )						->	None:


   """simple docstring"""







   super().__init__(**lowercase_)
   _UpperCamelCase           =    size if size is not None else {"shortest_edge": 224}
   _UpperCamelCase           =    get_size_dict(lowercase_							, default_to_square=lowercase_)
   _UpperCamelCase           =    crop_size if crop_size is not None else {"height": 224, "width": 224}
   _UpperCamelCase           =    get_size_dict(lowercase_							, default_to_square=lowercase_							, param_name="crop_size")

   _UpperCamelCase           =    do_resize
   _UpperCamelCase           =    size
   _UpperCamelCase           =    resample
   _UpperCamelCase           =    do_center_crop
   _UpperCamelCase           =    crop_size
   _UpperCamelCase           =    do_rescale
   _UpperCamelCase           =    rescale_factor
   _UpperCamelCase           =    do_normalize
   _UpperCamelCase           =    image_mean if image_mean is not None else OPENAI_CLIP_MEAN
   _UpperCamelCase           =    image_std if image_std is not None else OPENAI_CLIP_STD
   _UpperCamelCase           =    do_convert_rgb







 def 		__UpperCAmelCase				(		self				:   Optional[Any]							, lowercase_				:   np.ndarray							, lowercase_				:   Dict[str, int]							, lowercase_				:   PILImageResampling = PILImageResampling.BICUBIC							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   Dict							, )						->	np.ndarray:


   """simple docstring"""







   _UpperCamelCase           =    get_size_dict(lowercase_							, default_to_square=lowercase_)
   if "shortest_edge" not in size:
     raise ValueError(f'The `size` parameter must contain the key `shortest_edge`. Got {size.keys()}')
   _UpperCamelCase           =    get_resize_output_image_size(lowercase_							, size=size["shortest_edge"]							, default_to_square=lowercase_)
   return resize(lowercase_							, size=lowercase_							, resample=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   int							, lowercase_				:   np.ndarray							, lowercase_				:   Dict[str, int]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   Optional[Any]							, )						->	np.ndarray:


   """simple docstring"""







   _UpperCamelCase           =    get_size_dict(lowercase_)
   if "height" not in size or "width" not in size:
     raise ValueError(f'The `size` parameter must contain the keys (height, width). Got {size.keys()}')
   return center_crop(lowercase_							, size=(size["height"], size["width"])							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Any							, lowercase_				:   np.ndarray							, lowercase_				:   Union[int, float]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   List[Any]							, )						->	Optional[int]:


   """simple docstring"""







   return rescale(lowercase_							, scale=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Union[str, Any]							, lowercase_				:   np.ndarray							, lowercase_				:   Union[float, List[float]]							, lowercase_				:   Union[float, List[float]]							, lowercase_				:   Optional[Union[str, ChannelDimension]] = None							, **lowercase_				:   Tuple							, )						->	np.ndarray:


   """simple docstring"""







   return normalize(lowercase_							, mean=lowercase_							, std=lowercase_							, data_format=lowercase_							, **lowercase_)







 def 		__UpperCAmelCase				(		self				:   Dict							, lowercase_				:   ImageInput							, lowercase_				:   bool = None							, lowercase_				:   Dict[str, int] = None							, lowercase_				:   PILImageResampling = None							, lowercase_				:   bool = None							, lowercase_				:   int = None							, lowercase_				:   bool = None							, lowercase_				:   float = None							, lowercase_				:   bool = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   Optional[Union[float, List[float]]] = None							, lowercase_				:   bool = None							, lowercase_				:   Optional[Union[str, TensorType]] = None							, lowercase_				:   Optional[ChannelDimension] = ChannelDimension.FIRST							, **lowercase_				:   int							, )						->	PIL.Image.Image:


   """simple docstring"""







   _UpperCamelCase           =    do_resize if do_resize is not None else self.do_resize
   _UpperCamelCase           =    size if size is not None else self.size
   _UpperCamelCase           =    get_size_dict(lowercase_							, param_name="size"							, default_to_square=lowercase_)
   _UpperCamelCase           =    resample if resample is not None else self.resample
   _UpperCamelCase           =    do_center_crop if do_center_crop is not None else self.do_center_crop
   _UpperCamelCase           =    crop_size if crop_size is not None else self.crop_size
   _UpperCamelCase           =    get_size_dict(lowercase_							, param_name="crop_size"							, default_to_square=lowercase_)
   _UpperCamelCase           =    do_rescale if do_rescale is not None else self.do_rescale
   _UpperCamelCase           =    rescale_factor if rescale_factor is not None else self.rescale_factor
   _UpperCamelCase           =    do_normalize if do_normalize is not None else self.do_normalize
   _UpperCamelCase           =    image_mean if image_mean is not None else self.image_mean
   _UpperCamelCase           =    image_std if image_std is not None else self.image_std
   _UpperCamelCase           =    do_convert_rgb if do_convert_rgb is not None else self.do_convert_rgb

   _UpperCamelCase           =    make_list_of_images(lowercase_)

   if not valid_images(lowercase_):
     raise ValueError(
         "Invalid image type. Must be of type PIL.Image.Image, numpy.ndarray, "
         "torch.Tensor, tf.Tensor or jax.ndarray.")

   if do_resize and size is None:
     raise ValueError("Size must be specified if do_resize is True.")

   if do_center_crop and crop_size is None:
     raise ValueError("Crop size must be specified if do_center_crop is True.")

   if do_rescale and rescale_factor is None:
     raise ValueError("Rescale factor must be specified if do_rescale is True.")

   if do_normalize and (image_mean is None or image_std is None):
     raise ValueError("Image mean and std must be specified if do_normalize is True.")

   # PIL RGBA images are converted to RGB
   if do_convert_rgb:
     _UpperCamelCase           =    [convert_to_rgb(lowercase_) for image in images]

   # All transformations expect numpy arrays.
   _UpperCamelCase           =    [to_numpy_array(lowercase_) for image in images]

   if do_resize:
     _UpperCamelCase           =    [self.resize(image=lowercase_							, size=lowercase_							, resample=lowercase_) for image in images]

   if do_center_crop:
     _UpperCamelCase           =    [self.center_crop(image=lowercase_							, size=lowercase_) for image in images]

   if do_rescale:
     _UpperCamelCase           =    [self.rescale(image=lowercase_							, scale=lowercase_) for image in images]

   if do_normalize:
     _UpperCamelCase           =    [self.normalize(image=lowercase_							, mean=lowercase_							, std=lowercase_) for image in images]

   _UpperCamelCase           =    [to_channel_dimension_format(lowercase_							, lowercase_) for image in images]

   _UpperCamelCase           =    {"pixel_values": images}
   return BatchFeature(data=lowercase_							, tensor_type=lowercase_)





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_tokenizers_available,
    is_torch_available,
)


lowerCamelCase__  =					{
    '''configuration_deberta''': ['''DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP''', '''DebertaConfig''', '''DebertaOnnxConfig'''],
    '''tokenization_deberta''': ['''DebertaTokenizer'''],
}

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					['''DebertaTokenizerFast''']

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''DebertaForMaskedLM''',
       '''DebertaForQuestionAnswering''',
       '''DebertaForSequenceClassification''',
       '''DebertaForTokenClassification''',
       '''DebertaModel''',
       '''DebertaPreTrainedModel''',
   ]

try:
   if not is_tf_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   lowerCamelCase__  =					[
       '''TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST''',
       '''TFDebertaForMaskedLM''',
       '''TFDebertaForQuestionAnswering''',
       '''TFDebertaForSequenceClassification''',
       '''TFDebertaForTokenClassification''',
       '''TFDebertaModel''',
       '''TFDebertaPreTrainedModel''',
   ]


if TYPE_CHECKING:
   from .configuration_deberta import DEBERTA_PRETRAINED_CONFIG_ARCHIVE_MAP, DebertaConfig, DebertaOnnxConfig
   from .tokenization_deberta import DebertaTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_deberta_fast import DebertaTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_deberta import (
          DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          DebertaForMaskedLM,
          DebertaForQuestionAnswering,
          DebertaForSequenceClassification,
          DebertaForTokenClassification,
          DebertaModel,
          DebertaPreTrainedModel,
      )

   try:
      if not is_tf_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_tf_deberta import (
          TF_DEBERTA_PRETRAINED_MODEL_ARCHIVE_LIST,
          TFDebertaForMaskedLM,
          TFDebertaForQuestionAnswering,
          TFDebertaForSequenceClassification,
          TFDebertaForTokenClassification,
          TFDebertaModel,
          TFDebertaPreTrainedModel,
      )


else:
   import sys

   lowerCamelCase__  =					_LazyModule(__name__, globals()['''__file__'''], _import_structure, module_spec=__spec__)




